1887

Microbial Genomics logo

Volume 6, Issue 12

Comprehensive genome analyses of , a potential biomarker of homeostasis gut recovery Open Access

Abstract

is a Gram-positive and anaerobic bacterial species previously considered as uncultivable. Although little is known about this family member, its increased abundance has been reported in patients who have recovered from intestinal homeostasis after dysbiosis events. In this context, the aim of the present study was to take advantage of a massive culture protocol that allowed the recovery of extremely oxygen-sensitive species from faecal samples, which led to isolation of . Whole genome analyses of 11 . genomes revealed that this species has a highly conserved genome with 99.7 % 16S rRNA gene sequence similarity, average nucleotide polymorphism results >95, and 50.1 % of its coding potential being part of the core genome. Despite this, the variable portion of its genome was informative enough to reveal the existence of three lineages (lineage-I including isolates from Chile and France, lineage-II from South Korea and Finland, and lineage-III from China and one isolate from the USA) and evidence of some recombination signals. The identification of a cluster of orthologous groups revealed a high number of genes involved in metabolism, including amino acid and carbohydrate transport as well as energy production and conversion, which matches with the metabolic profile previously reported for microbiota from healthy individuals. Additionally, virulence factors and antimicrobial resistance genes were found (mainly in lineage-III), which could favour their survival during antibiotic-induced dysbiosis. These findings provide the basis of knowledge about the potential of as a bioindicator of intestinal homeostasis recovery and contribute to advancing the characterization of gut microbiota members with beneficial potential.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): extremely oxygen-sensitive species , gut homeostasis , phylogenomic and Sellimonas intestinalis
Funding
This study was supported by the:
  • Agencia Nacional de investigación y Desarrollo (Award FONDECYT Grant 1191601)
    • Principle Award Recipient: Daniel Paredes-Sabja
  • Agencia Nacional de investigación y Desarrollo (Award T020076)
    • Principle Award Recipient: Daniel Paredes-Sabja
  • Agencia Nacional de investigación y Desarrollo (Award Millennium Science Initiative Program – NCN17_09)
    • Principle Award Recipient: Daniel Paredes-Sabja
© 2020 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000476
2020-11-18
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/mgen/6/12/mgen000476.html?itemId=/content/journal/mgen/10.1099/mgen.0.000476&mimeType=html&fmt=ahah

References

  1. Barko PC, McMichael MA, Swanson KS, Williams DA. The gastrointestinal microbiome: a review. J Vet Intern Med 2018; 32:9–25 [View Article][PubMed]
     [Google Scholar]
  2. Rinninella E, Raoul P, Cintoni M, Franceschi F, Miggiano GAD et al. What is the healthy gut microbiota composition? a changing ecosystem across age, environment, diet, and diseases. Microorganisms 2019; 7:E1414 10 01 2019 [View Article][PubMed]
     [Google Scholar]
  3. Blaut M. Ecology and physiology of the intestinal tract. Curr Top Microbiol Immunol 2013; 358:247–272 [View Article][PubMed]
     [Google Scholar]
  4. Eberl G. The microbiota, a necessary element of immunity. C R Biol 2018; 341:281–283 [View Article][PubMed]
     [Google Scholar]
  5. Cong J, Zhang X. How human microbiome talks to health and disease. Eur J Clin Microbiol Infect Dis 2018; 37:1595–1601 [View Article][PubMed]
     [Google Scholar]
  6. Gupta VK, Paul S, Dutta C. Geography, ethnicity or Subsistence-Specific variations in human microbiome composition and diversity. Front Microbiol 2017; 8:1162 [View Article][PubMed]
     [Google Scholar]
  7. Carabeo-Pérez A, Guerra-Rivera G, Ramos-Leal M, Jiménez-Hernández J. Metagenomic approaches: effective tools for monitoring the structure and functionality of microbiomes in anaerobic digestion systems. Appl Microbiol Biotechnol 2019; 103:9379–9390 [View Article][PubMed]
     [Google Scholar]
  8. Fraher MH, O'Toole PW, Quigley EMM. Techniques used to characterize the gut microbiota: a guide for the clinician. Nat Rev Gastroenterol Hepatol 2012; 9:312–322 [View Article][PubMed]
     [Google Scholar]
  9. Mitchell SL, Simner PJ. Next-Generation sequencing in clinical microbiology: are we there yet?. Clin Lab Med 2019; 39:405–418 [View Article][PubMed]
     [Google Scholar]
  10. Lozupone CA, Stombaugh J, Gonzalez A, Ackermann G, Wendel D et al. Meta-Analyses of studies of the human microbiota. Genome Res 2013; 23:1704–1714 [View Article][PubMed]
     [Google Scholar]
  11. Suchodolski JS. Intestinal microbiota of dogs and cats: a bigger world than we thought. Vet Clin North Am Small Anim Pract 2011; 41:261–272 [View Article][PubMed]
     [Google Scholar]
  12. Meehan CJ, Beiko RG. A phylogenomic view of ecological specialization in the Lachnospiraceae, a family of digestive tract-associated bacteria. Genome Biol Evol 2014; 6:703–713 [View Article][PubMed]
     [Google Scholar]
  13. Brestoff JR, Artis D. Commensal bacteria at the interface of host metabolism and the immune system. Nat Immunol 2013; 14:676–684 [View Article][PubMed]
     [Google Scholar]
  14. Seo B, Yoo JE, Lee YM, Ko G. Sellimonas intestinalis gen. nov., sp. nov., isolated from human faeces. Int J Syst Evol Microbiol 2016; 66:951–956 [View Article][PubMed]
     [Google Scholar]
  15. Versluis D, de J Bello González T, Zoetendal EG, Passel MWJvan, Smidt H. High throughput cultivation-based screening on porous aluminum oxide chips allows targeted isolation of antibiotic resistant human gut bacteria. PLoS One 2019; 14:e0210970 [View Article][PubMed]
     [Google Scholar]
  16. Sun Y, Chen Q, Lin P, Xu R, He D et al. Characteristics of gut microbiota in patients with rheumatoid arthritis in Shanghai, China. Front Cell Infect Microbiol 2019; 9:369 [View Article][PubMed]
     [Google Scholar]
  17. Kong C, Gao R, Yan X, Huang L, He J et al. Alterations in intestinal microbiota of colorectal cancer patients receiving radical surgery combined with adjuvant CapeOx therapy. Sci China Life Sci 2019; 62:1178–1193 [View Article][PubMed]
     [Google Scholar]
  18. Liu Y, Li J, Jin Y, Zhao L, Zhao F et al. Splenectomy leads to amelioration of altered gut microbiota and metabolome in liver cirrhosis patients. Front Microbiol 2018; 9:963 [View Article][PubMed]
     [Google Scholar]
  19. Lun H, Yang W, Zhao S, Jiang M, Xu M et al. Altered gut microbiota and microbial biomarkers associated with chronic kidney disease. Microbiologyopen 2019; 8:e00678 [View Article][PubMed]
     [Google Scholar]
  20. Dong Y-Q, Wang W, Li J, Ma M-S, Zhong L-Q et al. Characterization of microbiota in systemic-onset juvenile idiopathic arthritis with different disease severities. World J Clin Cases 2019; 7:2734–2745 [View Article][PubMed]
     [Google Scholar]
  21. Poyet M, Groussin M, Gibbons SM, Avila-Pacheco J, Jiang X et al. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat Med 2019; 25:1442–1452 [View Article][PubMed]
     [Google Scholar]
  22. Siah SP, Merif J, Kaur K, Nair J, Huntington PG et al. Improved detection of gastrointestinal pathogens using generalised sample processing and amplification panels. Pathology 2014; 46:53–59 [View Article][PubMed]
     [Google Scholar]
  23. Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA et al. Culturing of 'unculturable' human microbiota reveals novel taxa and extensive sporulation. Nature 2016; 533:543–546 [View Article][PubMed]
     [Google Scholar]
  24. Dyke SO, Hubbard TJ. Developing and implementing an institute-wide data sharing policy. Genome Med 2011; 3:60 [View Article][PubMed]
     [Google Scholar]
  25. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 2017; 13:e1005595 [View Article][PubMed]
     [Google Scholar]
  26. Gualtero SM, Abril LA, Camelo N, Sanchez SD, Davila FA et al. [Characteristics of Clostridium difficile infection in a high complexity hospital and report of the circulation of the NAP1/027 hypervirulent strain in Colombia]. Biomedica 2017; 37:466–472 [View Article][PubMed]
     [Google Scholar]
  27. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108 [View Article][PubMed]
     [Google Scholar]
  28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. The Silva ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013; 41:D590–D596 [View Article][PubMed]
     [Google Scholar]
  29. Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T et al. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res 2014; 42:D581–D591 [View Article][PubMed]
     [Google Scholar]
  30. Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. PATRIC 3.5.4. Search criteria: Genomes/Clostridium. https://www.patricbrc.org/view/GenomeList/?keyword(clostridium)#view_tab=genomes&filter=eq(genome_status,%22Complete%22 ; 2017
  31. Silvester N, Alako B, Amid C, Cerdeño-Tarrága A, Clarke L et al. The European nucleotide Archive in 2017. Nucleic Acids Res 2018; 46:D36–D40 [View Article][PubMed]
     [Google Scholar]
  32. Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL et al. Database resources of the National center for biotechnology information. Nucleic Acids Res 2000; 28:10–14 [View Article][PubMed]
     [Google Scholar]
  33. Figueras MJ, Beaz-Hidalgo R, Hossain MJ, Liles MR. Taxonomic affiliation of new genomes should be verified using average nucleotide identity and multilocus phylogenetic analysis. Genome Announc 2014; 2:e00927-14 04 12 2014 [View Article][PubMed]
     [Google Scholar]
  34. Richter M, Rosselló-Móra R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci U S A 2009; 106:19126–19131 [View Article][PubMed]
     [Google Scholar]
  35. Grant JR, Stothard P. The CGView server: a comparative genomics tool for circular genomes. Nucleic Acids Res 2008; 36:W181–W184 [View Article][PubMed]
     [Google Scholar]
  36. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article][PubMed]
     [Google Scholar]
  37. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 2013; 29:2933–2935 [View Article][PubMed]
     [Google Scholar]
  38. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119 [View Article][PubMed]
     [Google Scholar]
  39. Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 2004; 32:11–16 [View Article][PubMed]
     [Google Scholar]
  40. Pruitt KD, Tatusova T, Brown GR, Maglott DR. Ncbi reference sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 2012; 40:D130–D135 [View Article][PubMed]
     [Google Scholar]
  41. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28:3150–3152 [View Article][PubMed]
     [Google Scholar]
  42. UniProt Consortium The universal protein resource (UniProt). Nucleic Acids Res 2008; 36:D190–195 [View Article][PubMed]
     [Google Scholar]
  43. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article][PubMed]
     [Google Scholar]
  44. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013; 30:772–780 [View Article][PubMed]
     [Google Scholar]
  45. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 2009; 26:1641–1650 [View Article][PubMed]
     [Google Scholar]
  46. Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ et al. Bayesian phylogenetic and phylodynamic data integration using beast 1.10. Virus Evol 2018; 4:vey016 [View Article][PubMed]
     [Google Scholar]
  47. Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat Methods 2012; 9:772 [View Article][PubMed]
     [Google Scholar]
  48. Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior Summarization in Bayesian phylogenetics using tracer 1.7. Syst Biol 2018; 67:901–904 [View Article][PubMed]
     [Google Scholar]
  49. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. Beast 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 2014; 10:e1003537 [View Article][PubMed]
     [Google Scholar]
  50. Letunic I, Bork P. Interactive tree of life (iTOL) V3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res 2016; 44:W242–W245 [View Article][PubMed]
     [Google Scholar]
  51. Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 2006; 23:254–267 [View Article][PubMed]
     [Google Scholar]
  52. Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res 2016; 44:D286–D293 [View Article][PubMed]
     [Google Scholar]
  53. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P et al. Card 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 2017; 45:D566–D573 [View Article][PubMed]
     [Google Scholar]
  54. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 2012; 67:2640–2644 [View Article][PubMed]
     [Google Scholar]
  55. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ et al. Using the NCBI AMRFinder tool to determine antimicrobial resistance genotype-phenotype correlations within a collection of NARMS isolates. bioRxiv 2019; 550707:
     [Google Scholar]
  56. Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother 2014; 58:212–220 [View Article][PubMed]
     [Google Scholar]
  57. Chen L, Zheng D, Liu B, Yang J, Jin Q. VFDB 2016: hierarchical and refined dataset for big data analysis--10 years on. Nucleic Acids Res 2016; 44:D694–D697 [View Article][PubMed]
     [Google Scholar]
  58. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014; 58:3895–3903 [View Article][PubMed]
     [Google Scholar]
  59. Hunt M, Mather AE, Sánchez-Busó L, Page AJ, Parkhill J et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom 2017; 3:e000131 [View Article][PubMed]
     [Google Scholar]
  60. Krasselt M, Baerwald C. Efficacy and safety of modified-release prednisone in patients with rheumatoid arthritis. Drug Des Devel Ther 2016; 10:1047–1058 [View Article][PubMed]
     [Google Scholar]
  61. Barkia I, Saari N, Manning SR. Microalgae for high-value products towards human health and nutrition. Mar Drugs 2019; 17:304 24 May 2019 [View Article][PubMed]
     [Google Scholar]
  62. Laudadio I, Fulci V, Palone F, Stronati L, Cucchiara S et al. Quantitative assessment of shotgun Metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. OMICS 2018; 22:248–254 [View Article][PubMed]
     [Google Scholar]
  63. Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N Engl J Med 2016; 375:2369–2379 [View Article][PubMed]
     [Google Scholar]
  64. Gillings MR, Paulsen IT, Tetu SG. Ecology and evolution of the human microbiota: fire, farming and antibiotics. Genes 2015; 6:841–857 [View Article][PubMed]
     [Google Scholar]
  65. Lagier J-C, Dubourg G, Million M, Cadoret F, Bilen M et al. Culturing the human microbiota and culturomics. Nat Rev Microbiol 2018; 16:540–550 [View Article][PubMed]
     [Google Scholar]
  66. Galperin MY, Brover V, Tolstoy I, Yutin N. Phylogenomic analysis of the family Peptostreptococcaceae (Clostridium cluster XI) and proposal for reclassification of Clostridium litorale (Fendrich et al. 1991) and Eubacterium acidaminophilum (Zindel et al. 1989) as Peptoclostridium litorale gen. nov. comb. nov. and Peptoclostridium acidaminophilum comb. nov. Int J Syst Evol Microbiol 2016; 66:5506–5513 [View Article][PubMed]
     [Google Scholar]
  67. Gerstein AC, Jean-Sébastien M. Small is the new big: assessing the population structure of microorganisms. Mol Ecol 2011; 20:4385–4387 [View Article][PubMed]
     [Google Scholar]
  68. Kung VL, Ozer EA, Hauser AR. The accessory genome of Pseudomonas aeruginosa. Microbiol Mol Biol Rev 2010; 74:621–641 [View Article][PubMed]
     [Google Scholar]
  69. Motayo BO, Oluwasemowo OO, Olusola BA, Opayele AV, Faneye AO. Phylogeography and evolutionary analysis of African rotavirus a genotype G12 reveals district genetic diversification within lineage III. Heliyon 2019; 5:e02680 [View Article][PubMed]
     [Google Scholar]
  70. Lin L, Zhang J. Role of intestinal microbiota and metabolites on gut homeostasis and human diseases. BMC Immunol 2017; 18:2 [View Article][PubMed]
     [Google Scholar]
  71. Fu X, Liu Z, Zhu C, Mou H, Kong Q. Nondigestible carbohydrates, butyrate, and butyrate-producing bacteria. Crit Rev Food Sci Nutr 2019; 59:S130–S152 [View Article][PubMed]
     [Google Scholar]
  72. Suzuki TA, Worobey M. Geographical variation of human gut microbial composition. Biol Lett 2014; 10:20131037 [View Article][PubMed]
     [Google Scholar]
  73. van Schaik W. The human gut resistome. Philos Trans R Soc Lond B Biol Sci 2015; 370:20140087 [View Article][PubMed]
     [Google Scholar]
  74. Ishikawa J, Chiba K, Kurita H, Satoh H. Contribution of rpoB2 RNA polymerase beta subunit gene to rifampin resistance in Nocardia species. Antimicrob Agents Chemother 2006; 50:1342–1346 [View Article][PubMed]
     [Google Scholar]
  75. Garcia-Gutierrez E, Mayer MJ, Cotter PD, Narbad A. Gut microbiota as a source of novel antimicrobials. Gut Microbes 2019; 10:1–21 [View Article][PubMed]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000476
Loading
Comprehensive genome analyses of Sellimonas intestinalis, a potential biomarker of homeostasis gut recovery
M Gen 6, e000476 (2020); https://doi.org/10.1099/mgen.0.000476
/content/journal/mgen/10.1099/mgen.0.000476
/content/journal/mgen/10.1099/mgen.0.000476
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error